2024_Hasegawa_Huang_et_al_CODE/Fig1/Fig1F.m

%% Fig. 1F - N of session to reach learning criterion

%% Code for Fig. 1F
clear
close all
% ==========================================================================
%% Boxplot of initial and reversal learning
% ==========================================================================
%Load source data at first or use the following variables
InitialLearning  = [6 15 11 7 5 14 7 15];
ReversalLearning = [5 10 17 9 7 9 11 7];

%Initial Learning boxplot--------------------------------------------------
sz = 20;
fig1 = figure('Name', 'NofSession2Learn_Initial');
fig1.Position = [1430 480 230 420];
boxplot(InitialLearning, 'MedianStyle', 'line', 'Labels',{'Initial'})
hold on
h1 = scatter(ones(size(InitialLearning)).*(1+(rand(size(InitialLearning)))/10-0.03), InitialLearning, 'b', 'filled', 'MarkerFaceAlpha', 0.5);
h1.SizeData = 40;

%Lim and ticks
ylim([0 18]);
yticks([0 5 10 15 20]);

%Label and title
ylabel('N of Sessions')
title('INI - N of Session to criterion')
box off

%Copy object
ax_Ini = gca;

%Reversal Learning boxplot-------------------------------------------------
sz = 20;
fig2 = figure('Name', 'NofSession2Learn_Reversal');
fig2.Position = [1430 480 230 420];
boxplot(ReversalLearning, 'MedianStyle', 'line', 'Labels',{'Reversal'})
hold on
h2 = scatter(ones(size(ReversalLearning)).*(1+(rand(size(ReversalLearning)))/10-0.03), ReversalLearning, 'r', 'filled', 'MarkerFaceAlpha', 0.5);
h2.SizeData = 40;

%Lim and ticks
ylim([0 18]);
yticks([0 5 10 15 20]);

%Label and title
ylabel('N of Sessions')
title('REV - N of Session to criterion')
box off